1,721,543 research outputs found
Spatio-temporal evolution of wet-dry event features and their transition across the Upper Jhelum Basin (UJB) in South Asia
Full text linkThe increasing rate of occurrence of extreme events (droughts and floods) and their rapid transition magnify the associated socio-economic impacts with respect to those caused by the individual event. Understanding of spatio-temporal evolution of wet-dry events collectively, their characteristics, and the transition (wet to dry and dry to wet) is therefore significant to identify and locate most vulnerable hotspots, providing the basis for the adaptation and mitigation measures. The Upper Jhelum Basin (UJB) in South Asia was selected as a case study, where the relevance of wet-dry events and their transition has not been assessed yet, despite clear evidence of climate change in the region. The standardized precipitation evapotranspiration index (SPEI) at the monthly timescale was applied to detect and characterize wet and dry events for the period 1981-2014. The results of temporal variations in SPEI showed a strong change in basin climatic features associated with El Niño-Southern Oscillation (ENSO) at the end of 1997, with the prevalence of wet and dry events before and after 1997 respectively. The results of spatial analysis show a higher susceptibility of the monsoon-dominated region towards wet events, with more intense events occurring in the eastern part, whereas a higher severity and duration are featured in the southwestern part of the basin. In contrast, the westerlies-dominated region was found to be the hotspot of dry events with higher duration, severity, and intensity. Moreover, the surrounding region of the Himalaya divide line and the monsoon-dominated part of the basin were found to be the hotspots of rapid wet-dry transition events. Copyright
Computation of Invariant Tori in the Hill Restricted Four-Body Problem via Collocation
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Trend analysis of precipitation, temperature and snow water equivalent in Lombardy region, northern Italy
No full textThe analysis of precipitation, temperature, and snow water equivalent (SWE) trends offers a scientific approach for understanding the impacts of climate change. This paper presents a comprehensive analysis of climate change indicators in Lombardy, Italy, covering the yearly and monthly trends of precipitation and temperature from 1990 to 2020. Additionally, the yearly and monthly SWE variations. For this purpose, a range of statistical tests have been used including Mann–Kendall, Pettitt’s change point detection and Sen’s slope estimator. Precipitation trend shows a slight annual increase of around 5.42 mm per year. This trend is not statistically significant with a Mann–Kendall p value of 0.1448 and no changing point has been detected. Moreover, seasonal precipitation patterns show minor variations, with Kendall’s Tau values ranging from − 0.0431 to 0.1761. However, none of these trends are statistically significant, as reflected in Mann–Kendall p values ranging from 0.1679 to 0.7339. Conversely, Lombardy is experiencing a significant annual temperature increase of 0.0436 °C. Notably, in Autumn, temperatures rise at a rate of 0.0565° per year. Summer also shows a significant warming trend, with temperatures increasing by 0.0421° per year. In Winter and Spring, there are milder, non-significant temperature trends, with Kendall’s Tau values around 0.17. SWE trend shows minor annual variations (5–10%) and monthly shifts. Winter shows a slight decrease (2–3%), implying delayed snow accumulation, while Spring indicates minor change (1–2%) suggesting earlier snowmelt. This study emphasizes temperature's strong impact on SWE and stresses the importance of climate monitoring and collaboration for understanding changing climate patterns
Predicting the impact of climate change on urban drainage systems in northwestern Italy by a copula-based approach
Full text linkStudy region: Milan, northwestern Italy.
Study focus: The impact of expected trends in storm temporal structures is analyzed with reference to urban drainage systems, featuring catchment areas spanning from 10 ha to 100 ha. A bivariate stochastic model for the derivation of flood frequency is developed, accounting for the seasonality of storm volumes, durations and their mutual dependence structure. Its reliability is verified by comparing it to continuous hydrodynamic simulations. To do so, a 21-year long series observed at Milan-Monviso raingauge was used. Model comparison evidences a satisfactory agreement between models.
New hydrological insights for the region: Although the total annual precipitation is not expected to change, relevant increases in flood frequencies are predicted. Such increases vary between 10-20% and appear to be independent of the return period. Thus, great concerns arise for the existing urban drainage systems located in northwestern Italy, which should basically be unable to face these flood frequency changes. A leading role is played by the intensification of summer and spring storms, both in terms of increase in volumes and decrease in durations. Moreover, changes in the dependence structure have a significant impact when summer storms are considered. Conversely, flood frequency curves are far less sensitive to the storm temporal structures featuring other seasons. These results can be explained by considering the seasonal distribution of storms critical for urban drainage systems
Global uncertainty assessment in hydrological forecasting by means of statistical analysis of forecast errors
No full textImmersive Monuments : Social Memory and Trauma Processing in Video Games and Virtual Reality
No full textBy monument, we mean in this text a narrative and spatialized artifact that allows the reactivation of the past. In this respect, we will consider as monuments the experiments made possible by the new interactive digital technologies such as video games and virtual and augmented reality (VR and AR). Following the perspective opened by the most recent studies on memory, we will question the contemporary processes of understanding historical events, which today necessarily involve a mediated repositioning of the personal point of view and emotions
Performance evaluation of raw and bias-corrected ERA5 precipitation data with respect to extreme precipitation analysis: case study in Upper Jhelum Basin, South Asia
No full textThe application of gridded precipitation datasets as a substitute of limited ground observation over mountainous regions is challenging due to considerable biases and needs adjustments before their application in subsequent impact models. In this study, four commonly used precipitation bias correction (BC) methods were evaluated for their skills to capture various aspects of extreme precipitation over Upper Jhelum Basin (UJB) for a period of 34 years (1981–2014). The four BC methods, i.e., linear scaling (LS), local scaling intensity (LOCI), power transmission (PT), and distribution mapping (DM), were applied on ERA5 reanalysis precipitation dataset and evaluated using nine extreme precipitation indices. First, it was found that the raw/original ERA5 overestimates observed precipitation and number of wet days with little precipitation and thus inevitability needs correction in raw estimates. Second, more or less all BC methods improved the raw ERA5 estimates especially magnitude; however, clear discrepancies exist in their skills to correct wet day frequency. Overall, the DM method was found to be a good compromise to correct various aspects of extreme precipitation, followed by LOCI, PT, and LS methods. This study provides twofold potential benefits; firstly, extreme precipitation information tailored the need of relevant decision makers to devise appropriate mitigation and adaptation strategies, and secondly, provides a certain reference for evaluation, correction, and application of gridded datasets for extreme precipitation analysis in data-sparse region
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